Title: | Optimal Experimental Designs for Accelerated Life Testing |
---|---|
Description: | Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) <doi:10.32614/RJ-2015-029>. |
Authors: | Kangwon Seo [aut, cre], Rong Pan [aut] |
Maintainer: | Kangwon Seo <[email protected]> |
License: | GPL-3 |
Version: | 0.1.2 |
Built: | 2025-03-13 03:06:10 UTC |
Source: | https://github.com/cran/ALTopt |
Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) <doi:10.32614/RJ-2015-029>.
Package: | ALTopt |
Version: | 0.1.2 |
Authors@R: | as.person(c( "Kangwon Seo <[email protected]> [aut, cre]", "Rong Pan <[email protected]> [aut]" )) |
Depends: | R (>= 3.0.0) |
License: | GPL-3 |
LazyData: | true |
Imports: | cubature (>= 1.0), lattice (>= 0.20), methods |
Built: | R 3.6.1; ; 2019-12-12 12:30:00 UTC; windows |
Index:
alteval.ic Design evaluation with interval censoring. alteval.rc Design evaluation with right censoring. altopt.ic Optimal design with interval censoring. altopt.rc Optimal design with right censoring. compare.fus Comparing designs using FUS compare.vdus Comparing designs using VDUS convert.stress.level Coding and decoding stress level design.plot Design plot. pv.contour.ic Contour plot of prediction variance for a design with interval censoring. pv.contour.rc Contour plot of prediction variance for a design with right censoring. pv.fus.ic FUS (Fraction of Use Space) plot for interval censoring. pv.fus.rc FUS (Fraction of Use Space) plot for right censoring. pv.vdus.ic VDUS (Variance Dispersion of Use Space) plot for interval censoring. pv.vdus.rc VDUS (Variance Dispersion of Use Space) plot for right censoring.
Kangwon Seo [aut, cre], Rong Pan [aut]
Maintainer: Kangwon Seo <[email protected]>
Seo, K. and Pan, R. (2015) ALTopt: An R Package for Optimal Experimental Design of Accelerated Life Testing. The R Journal, 7(2), 177-188.
Monroe, E. M., Pan, R., Anderson-Cook, C. M., Montgomery, D. C. and Borror C. M. (2011) A Generalized Linear Model Approach to Designing Accelerated Life Test Experiments, Quality and Reliability Engineering International 27(4), 595–607
Yang, T., Pan, R. (2013) A Novel Approach to Optimal Accelerated Life Test Planning With Interval Censoring, Reliability, IEEE Transactions on 62(2), 527–536
altopt.rc, altopt.ic, alteval.rc, alteval.ic, pv.contour.rc, pv.contour.ic, pv.fus.rc, pv.fus.ic, pv.vdus.rc, pv.vdus.ic, compare.fus, compare.vdus,
design.plot, convert.stress.level
# D optimal design of two stress factors with right censoring. Design.D <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) design.plot(Design.D$opt.design.rounded, x1, x2) pv.contour.rc(Design.D$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.D <- pv.fus.rc(Design.D$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # U optimal design of two stress factors with right censoring. Design.U <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) design.plot(Design.U$opt.design.rounded, x1, x2) pv.contour.rc(Design.U$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.U <- pv.fus.rc(Design.U$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal design. compare.fus(FUS.D, FUS.U)
# D optimal design of two stress factors with right censoring. Design.D <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) design.plot(Design.D$opt.design.rounded, x1, x2) pv.contour.rc(Design.D$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.D <- pv.fus.rc(Design.D$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # U optimal design of two stress factors with right censoring. Design.U <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) design.plot(Design.U$opt.design.rounded, x1, x2) pv.contour.rc(Design.U$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.U <- pv.fus.rc(Design.U$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal design. compare.fus(FUS.D, FUS.U)
alteval.ic
calculates the objective function value
(D, U or I) for a given design with interval censoring plan.
alteval.ic( designTable, optType, t, k, nf, alpha, formula, coef, useCond, useLower, useUpper )
alteval.ic( designTable, optType, t, k, nf, alpha, formula, coef, useCond, useLower, useUpper )
designTable |
a data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
optType |
the choice of |
t |
the total testing time. |
k |
the number of time intervals. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the numeric vector of use condition.
It should be provided when |
useLower |
the numeric vector of lower bound of use region.
It should be provided when |
useUpper |
the numeric vector of upper bound of use region.
It should be provided when |
The objective function value corresponded by optType
for a given design with interval censoring plan.
# Evaluation of factorial design for interval censoring. x1 <- c(0, 1, 0, 1) x2 <- c(0, 0, 1, 1) allocation <- c(25, 25, 25, 25) facDes <- data.frame(x1, x2, allocation) alteval.ic(facDes, "D", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) alteval.ic(facDes, "U", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) alteval.ic(facDes, "I", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
# Evaluation of factorial design for interval censoring. x1 <- c(0, 1, 0, 1) x2 <- c(0, 0, 1, 1) allocation <- c(25, 25, 25, 25) facDes <- data.frame(x1, x2, allocation) alteval.ic(facDes, "D", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) alteval.ic(facDes, "U", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) alteval.ic(facDes, "I", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
alteval.rc
calculates the objective function value
(D, U or I) for a given design with right censoring plan.
alteval.rc( designTable, optType, tc, nf, alpha, formula, coef, useCond, useLower, useUpper )
alteval.rc( designTable, optType, tc, nf, alpha, formula, coef, useCond, useLower, useUpper )
designTable |
a data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
optType |
the choice of |
tc |
the censoring time. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the numeric vector of use condition.
It should be provided when |
useLower |
the numeric vector of lower bound of use region.
It should be provided when |
useUpper |
the numeric vector of upper bound of use region.
It should be provided when |
The objective function value corresponded by optType
for a given design with right censoring plan.
# Evaluation of factorial design for right censoring. x1 <- c(0, 1, 0, 1) x2 <- c(0, 0, 1, 1) allocation <- c(25, 25, 25, 25) facDes <- data.frame(x1, x2, allocation) alteval.rc(facDes, "D", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) alteval.rc(facDes, "U", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) alteval.rc(facDes, "I", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
# Evaluation of factorial design for right censoring. x1 <- c(0, 1, 0, 1) x2 <- c(0, 0, 1, 1) allocation <- c(25, 25, 25, 25) facDes <- data.frame(x1, x2, allocation) alteval.rc(facDes, "D", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) alteval.rc(facDes, "U", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) alteval.rc(facDes, "I", 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
altopt.ic
creates D, U or I optimal design
of the accelerated life testing with interval censoring plan.
altopt.ic( optType, N, t, k, nf, alpha, formula, coef, useCond, useLower, useUpper, nOpt = 1, nKM = 30, nCls = NULL )
altopt.ic( optType, N, t, k, nf, alpha, formula, coef, useCond, useLower, useUpper, nOpt = 1, nKM = 30, nCls = NULL )
optType |
the choice of |
N |
the number of test units. |
t |
the total testing time. |
k |
the number of time intervals. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the numeric vector of use condition.
It should be provided when |
useLower |
the numeric vector of lower bound of use region.
It should be provided when |
useUpper |
the numeric vector of upper bound of use region.
It should be provided when |
nOpt |
the number of repetition of optimization process. Default is 1. |
nKM |
the number of repetition of k-means clustering. Default is 20. |
nCls |
the number of clusters used for k-means clustering. If not specified, it is set as the number of parameters in the linear predictor model. |
A list with components
call: the matched call.
opt.design.ori: the original optimal design.
opt.value.ori: the objective function value of opt.design.ori
.
opt.design.rounded: the optimal design clustered by rounding in third decimal points.
opt.value.rounded: the objective function value of opt.design.rounded
.
opt.design.kmeans: the optimal design clustered by kmeans
.
opt.value.kmeans: the objective function value of opt.design.kmeans
.
Monroe, E. M., Pan, R., Anderson-Cook, C. M., Montgomery, D. C. and Borror C. M. (2011) A Generalized Linear Model Approach to Designing Accelerated Life Test Experiments, Quality and Reliability Engineering International 27(4), 595–607
Yang, T., Pan, R. (2013) A Novel Approach to Optimal Accelerated Life Test Planning With Interval Censoring, Reliability, IEEE Transactions on 62(2), 527–536
## Not run: # Generating D optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Generating U optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) # Generating I optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # Generating D optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Generating U optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) # Generating I optimal design for interval censoring. altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
altopt.rc
creates D, U or I optimal design
of the accelerated life testing with right censoring plan.
altopt.rc( optType, N, tc, nf, alpha, formula, coef, useCond, useLower, useUpper, nOpt = 1, nKM = 30, nCls = NULL )
altopt.rc( optType, N, tc, nf, alpha, formula, coef, useCond, useLower, useUpper, nOpt = 1, nKM = 30, nCls = NULL )
optType |
the choice of |
N |
the number of test units. |
tc |
the censoring time. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the numeric vector of use condition.
It should be provided when |
useLower |
the numeric vector of lower bound of use region.
It should be provided when |
useUpper |
the numeric vector of upper bound of use region.
It should be provided when |
nOpt |
the number of repetition of optimization process. Default is 1. |
nKM |
the number of repetition of k-means clustering. Default is 20. |
nCls |
the number of clusters used for k-means clustering. If not specified, it is set as the number of parameters in the linear predictor model. |
A list with components
call: the matched call.
opt.design.ori: the original optimal design.
opt.value.ori: the objective function value of opt.design.ori
.
opt.design.rounded: the optimal design clustered by rounding in third decimal points.
opt.value.rounded: the objective function value of opt.design.rounded
.
opt.design.kmeans: the optimal design clustered by kmeans
.
opt.value.kmeans: the objective function value of opt.design.kmeans
.
Monroe, E. M., Pan, R., Anderson-Cook, C. M., Montgomery, D. C. and Borror C. M. (2011) A Generalized Linear Model Approach to Designing Accelerated Life Test Experiments, Quality and Reliability Engineering International 27(4), 595–607
Yang, T., Pan, R. (2013) A Novel Approach to Optimal Accelerated Life Test Planning With Interval Censoring, Reliability, IEEE Transactions on 62(2), 527–536
## Not run: # Generating D optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Generating U optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) # Generating I optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # Generating D optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Generating U optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) # Generating I optimal design for right censoring. altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
compare.fus
draws the FUS plots of multiple designs on a
single frame.
compare.fus(...)
compare.fus(...)
... |
FUS plots of multiple designs.
## Not run: # Generating D optimal design and FUS plot. Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) FUS.D <- pv.fus.rc(Dopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Generating U optimal design and FUS plot. Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.U <- pv.fus.rc(Uopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal designs. compare.fus(FUS.D, FUS.U) ## End(Not run)
## Not run: # Generating D optimal design and FUS plot. Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) FUS.D <- pv.fus.rc(Dopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Generating U optimal design and FUS plot. Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) FUS.U <- pv.fus.rc(Uopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal designs. compare.fus(FUS.D, FUS.U) ## End(Not run)
compare.vdus
draws the VDUS plots of multiple designs on a
single frame.
compare.vdus(...)
compare.vdus(...)
... |
Objects created by |
VDUS plots of multiple designs.
## Not run: # Generating D optimal design and VDUS plot. Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) VDUS.D <- pv.vdus.rc(Dopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Generating U optimal design and VDUS plot. Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) VDUS.U <- pv.vdus.rc(Uopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal designs. compare.vdus(VDUS.D, VDUS.U) ## End(Not run)
## Not run: # Generating D optimal design and VDUS plot. Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) VDUS.D <- pv.vdus.rc(Dopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Generating U optimal design and VDUS plot. Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) VDUS.U <- pv.vdus.rc(Uopt$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) # Comparing D and U optimal designs. compare.vdus(VDUS.D, VDUS.U) ## End(Not run)
Convert the stress levels from the actual levels to standardized levels, and vice versa.
convert.stress.level(lowStLv, highStLv, actual = NULL, stand = NULL)
convert.stress.level(lowStLv, highStLv, actual = NULL, stand = NULL)
lowStLv |
a numeric vector containing the actual lowest stress level of each stress variable in design region. |
highStLv |
a numeric vector containing the actual highest stress level of each stress variable in design region. |
actual |
a data frame or numeric vector containing the design points in actual units. |
stand |
a data frame or numeric vector containing the design points in standardized units. |
When actual
is provided, the function converts it to the
standardized units and when stand
is provided, the function converts
it to the actual units.
## Not run: # Generating D optimal design in coded unit. Design <- altopt.rc(optType = "D", N = 100, tc = 100, nf = 2, alpha = 1, formula = ~x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Transform the coded unit to actual stress variable's level. convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5), stand = Design$opt.design.rounded) # Transform the actual stress level to coded units. use <- c(38.281, 3.219) convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5), actual = use) ## End(Not run)
## Not run: # Generating D optimal design in coded unit. Design <- altopt.rc(optType = "D", N = 100, tc = 100, nf = 2, alpha = 1, formula = ~x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) # Transform the coded unit to actual stress variable's level. convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5), stand = Design$opt.design.rounded) # Transform the actual stress level to coded units. use <- c(38.281, 3.219) convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5), actual = use) ## End(Not run)
design.plot
draws design plot as a form of a bubble plot
of any two stress factors which are specified by xAxis
and yAxis
.
The size of each bubble indicates the relative magnitude of allocation on
each design point.
design.plot(design, xAxis, yAxis)
design.plot(design, xAxis, yAxis)
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
xAxis |
the name of the factor to be displayed in x axis. |
yAxis |
the name of the factor to be displayed in y axis. |
The bubble plot of a design with two stress factors.
## Not run: # Design plot of D optimal design with right censoring. Design1 <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) design.plot(Design1$opt.design.rounded, x1, x2) ## End(Not run)
## Not run: # Design plot of D optimal design with right censoring. Design1 <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01)) design.plot(Design1$opt.design.rounded, x1, x2) ## End(Not run)
pv.contour.ic
draws the contour plot of prediction variance
for a given design with interval censoring plan. Either useCond
or
use region (useLower
and useUpper
) should be
provided.
pv.contour.ic( design, xAxis, yAxis, t, k, nf, alpha, formula, coef, useCond = NULL, useLower = NULL, useUpper = NULL )
pv.contour.ic( design, xAxis, yAxis, t, k, nf, alpha, formula, coef, useCond = NULL, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
xAxis |
the name of the factor to be displayed in x axis. |
yAxis |
the name of the factor to be displayed in y axis. |
t |
the total testing time. |
k |
the number of time intervals. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the vector of specified use condition. If it is provided, the contour line will be generated up to this point. |
useLower , useUpper
|
the vector of the use region. If these are
provided, the contour line will be generated up to this region.
Note that either |
The contour plot of prediction variance for interval censoring.
## Not run: # Contour plot of prediction variance of U optimal design with interval censoring. Design <- altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) pv.contour.ic(Design$opt.design.rounded, x1, x2, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) ## End(Not run)
## Not run: # Contour plot of prediction variance of U optimal design with interval censoring. Design <- altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) pv.contour.ic(Design$opt.design.rounded, x1, x2, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) ## End(Not run)
pv.contour.rc
draws the contour plot of prediction variance
for a given design with right censoring plan. Either useCond
or
use region (useLower
and useUpper
) should be
provided.
pv.contour.rc( design, xAxis, yAxis, tc, nf, alpha, formula, coef, useCond = NULL, useLower = NULL, useUpper = NULL )
pv.contour.rc( design, xAxis, yAxis, tc, nf, alpha, formula, coef, useCond = NULL, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
xAxis |
the name of the factor to be displayed in x axis. |
yAxis |
the name of the factor to be displayed in y axis. |
tc |
the censoring time. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useCond |
the vector of specified use condition. If it is provided, the contour line will be generated up to this point. |
useLower , useUpper
|
the vector of the use region. If these are
provided, the contour line will be generated up to this region.
Note that either |
The contour plot of prediction variance for right censoring.
## Not run: # Contour plot of prediction variance of U optimal design with right censoring. Design <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) pv.contour.rc(Design$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) ## End(Not run)
## Not run: # Contour plot of prediction variance of U optimal design with right censoring. Design <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) pv.contour.rc(Design$opt.design.rounded, x1, x2, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159)) ## End(Not run)
pv.fus.ic
draws the FUS plot of prediction variance
for a given design with interval censoring plan. The use region
(useLower
and useUpper
) should be
provided.
pv.fus.ic( design, t, k, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
pv.fus.ic( design, t, k, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
t |
the total testing time. |
k |
the number of time intervals. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useLower , useUpper
|
the vectors containing the lower bound and upper bound for the use region. They should be provided for FUS plot. |
The "trellis" object which includes the FUS plot for interval censoring.
## Not run: # FUS plot of I optimal design with interval censoring. Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.fus.ic(Design$opt.design.rounded, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # FUS plot of I optimal design with interval censoring. Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.fus.ic(Design$opt.design.rounded, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
pv.fus.rc
draws the FUS plot of prediction variance
for a given design with right censoring plan. The use region
(useLower
and useUpper
) should be
provided.
pv.fus.rc( design, tc, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
pv.fus.rc( design, tc, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
tc |
the censoring time. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useLower , useUpper
|
the vectors containing the lower bound and upper bound for the use region. They should be provided for FUS plot. |
The "trellis" object which includes the FUS plot for right censoring.
## Not run: # FUS plot of I optimal design with right censoring. Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.fus.rc(Design$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # FUS plot of I optimal design with right censoring. Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.fus.rc(Design$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
pv.vdus.ic
draws the VDUS plot of prediction variance
for a given design with interval censoring plan. The use region
(useLower
and useUpper
) should be
provided.
pv.vdus.ic( design, t, k, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
pv.vdus.ic( design, t, k, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
t |
the total testing time. |
k |
the number of time intervals. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useLower , useUpper
|
the vectors containing the lower bound and upper bound for the use region. They should be provided for VDUS plot. |
The "trellis" object which includes the VDUS plot for interval censoring.
## Not run: # VDUS plot of I optimal design with interval censoring. Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.vdus.ic(Design$opt.design.rounded, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # VDUS plot of I optimal design with interval censoring. Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.vdus.ic(Design$opt.design.rounded, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
pv.vdus.rc
draws the VDUS plot of prediction variance
for a given design with right censoring plan. The use region
(useLower
and useUpper
) should be
provided.
pv.vdus.rc( design, tc, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
pv.vdus.rc( design, tc, nf, alpha, formula, coef, useLower = NULL, useUpper = NULL )
design |
the data frame containing the coordinates and the number of
allocation of each design point. The design created by either
|
tc |
the censoring time. |
nf |
the number of stress factors. |
alpha |
the value of the shape parameter of Weibull distribution. |
formula |
the object of class formula which is the linear predictor model. |
coef |
the numeric vector containing the coefficients of each term in |
useLower , useUpper
|
the vectors containing the lower bound and upper bound for the use region. They should be provided for VDUS plot. |
The "trellis" object which includes the VDUS plot for right censoring.
## Not run: # VDUS plot of I optimal design with right censoring. Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.vdus.rc(Design$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)
## Not run: # VDUS plot of I optimal design with right censoring. Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) pv.vdus.rc(Design$opt.design.rounded, 100, 2, 1, formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459)) ## End(Not run)